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Average-case complexity

About: Average-case complexity is a research topic. Over the lifetime, 1749 publications have been published within this topic receiving 44972 citations.


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TL;DR: This paper examines the time complexity of computing the sum of two n-bit numbers, where the input numbers are encoded in an initial seed assembly, and the output sum is encoded in the final, terminal assembly of the system.
Abstract: In this paper we consider the time complexity of computing the sum and product of two $n$-bit numbers within the tile self-assembly model. The (abstract) tile assembly model is a mathematical model of self-assembly in which system components are square tiles with different glue types assigned to tile edges. Assembly is driven by the attachment of singleton tiles to a growing seed assembly when the net force of glue attraction for a tile exceeds some fixed threshold. Within this frame work, we examine the time complexity of computing the sum or product of 2 n-bit numbers, where the input numbers are encoded in an initial seed assembly, and the output is encoded in the final, terminal assembly of the system. We show that the problems of addition and multiplication have worst case lower bounds of $\Omega(\sqrt{n})$ in 2D assembly, and $\Omega(\sqrt[3]{n})$ in 3D assembly. In the case of addition, we design algorithms for both 2D and 3D that meet this bound with worst case run times of $O(\sqrt{n})$ and $O(\sqrt[3]{n})$ respectively, which beats the previous best known upper bound of O(n). Further, we consider average case complexity of addition over uniformly distributed n-bit strings and show how to achieve $O(\log n)$ average case time with a simultaneous $O(\sqrt{n})$ worst case run time in 2D. For multiplication, we present an $O(n^{5/6})$ time multiplication algorithm which works in 3D, which beats the previous best known upper bound of O(n). As additional evidence for the speed of our algorithms, we implement our addition algorithms, along with the simpler O(n) time addition algorithm, into a probabilistic run-time simulator and compare the timing results.

2 citations

Journal Article
TL;DR: Focusing on the analysis of binary sequences with period 2mpn, an anolog of the extended Games-Chan algorithm is provided and a tight upper bound for the 2-adic complexity is determined.
Abstract: The 2-adic complexity of a sequence had been taken an important standard to judge whether a sequence was safe or not, because of rational approximation algorithm Focusing on the analysis of binary sequences with period 2mpn, provided an anolog of the extended Games-Chan algorithm And furthermore, a tight upper bound for the 2-adic complexity is determined

2 citations

12 Oct 1993
TL;DR: It is proved that the VC*- dimension problem is polynomial-time reducible to the satisfiability problem of length J with O(log2J) variables, which includes the original VC-dimension problem as a special case.
Abstract: In the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to estimate the polynomial-sample learnability of a class of binary functions. For a class of {0,..., m}-valued functions, the notion has been generalized in various ways. This paper investigates the complexity of computing some of generalized VC-dimensions: VC*-dimension, Ψ*-dimension, and ΨG-dimension. For each dimension, we consider a decision problem that is, for a given matrix representing a class F of functions and an integer K, to determine whether the dimension of F is greater than K or not. We prove that the VC*-dimension problem is polynomial-time reducible to the satisfiability problem of length J with O(log2J) variables, which includes the original VC-dimension problem as a special case. We also show that the ΨG-dimension problem is still reducible to the satisfiability problem of length J with O(log2 J), while the Ψ*-dimension problem becomes NP-complete.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
20216
202010
20199
201810
201732